


import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase__							:						List[Any]							     =   logging.get_logger()
@dataclass
class 		__magic_name__						:





     '''simple docstring'''



     __lowercase				:							nn.Module
     __lowercase				:							List[nn.Module]	   =       field(default_factory=snake_case_				)
     __lowercase				:							list	   =       field(default_factory=snake_case_				)

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict     ,		_a:str     ,		_a:Tensor     ,		_a:Tensor   ):
       snake_case__						= len(list(m.modules()   )   ) == 1 or isinstance(_a     ,		nn.Convad   ) or isinstance(_a     ,		nn.BatchNormad   )
       if has_not_submodules:
         self.traced.append(_a   )

     def __call__(					self:str     ,		_a:Tensor   ):
       for m in self.module.modules():
         self.handles.append(m.register_forward_hook(self._forward_hook   )   )
       self.module(_a   )
       [x.remove() for x in self.handles]
       return self







     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       # check the len of the state_dict keys to see if we have learnable params
       return list(filter(lambda _a   : len(list(x.state_dict().keys()   )   ) > 0     ,		self.traced   )   )




@dataclass
class 		__magic_name__						:





     '''simple docstring'''



     __lowercase				:							nn.Module
     __lowercase				:							nn.Module
     __lowercase				:							int	   =       0
     __lowercase				:							List	   =       field(default_factory=snake_case_				)
     __lowercase				:							List	   =       field(default_factory=snake_case_				)

     def __call__(					self:Tuple     ,		_a:Tensor   ):
       snake_case__						= Tracker(self.dest   )(_a   ).parametrized
       snake_case__						= Tracker(self.src   )(_a   ).parametrized

       snake_case__						= list(filter(lambda _a   : type(_a   ) not in self.src_skip     ,		_a   )   )
       snake_case__						= list(filter(lambda _a   : type(_a   ) not in self.dest_skip     ,		_a   )   )

       if len(_a   ) != len(_a   ):
         raise Exception(
             F"""Numbers of operations are different. Source module has {len(_a   )} operations while"""
             F""" destination module has {len(_a   )}."""   )

       for dest_m, src_m in zip(_a     ,		_a   ):
         dest_m.load_state_dict(src_m.state_dict()   )
         if self.verbose == 1:
           print(F"""Transfered from={src_m} to={dest_m}"""   )







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase = True				)    ->				int:
  print(F"""Converting {name}..."""				)
  with torch.no_grad():
    snake_case__						= timm.create_model(__lowerCAmelCase							,   pretrained=__lowerCAmelCase				).eval()
    snake_case__						= ResNetForImageClassification(__lowerCAmelCase				).eval()
    snake_case__						= ModuleTransfer(src=__lowerCAmelCase							,   dest=__lowerCAmelCase				)
    snake_case__						= torch.randn((1, 3, 224, 224)				)
    module_transfer(__lowerCAmelCase				)

  assert torch.allclose(from_model(__lowerCAmelCase				)							,   our_model(__lowerCAmelCase				).logits				), "The model logits don't match the original one."

  snake_case__						= F"""resnet{'-'.join(name.split('resnet'				)				)}"""
  print(__lowerCAmelCase				)

  if push_to_hub:
    our_model.push_to_hub(
        repo_path_or_name=save_directory / checkpoint_name							,   commit_message='''Add model'''							,   use_temp_dir=__lowerCAmelCase							,   )

    # we can use the convnext one
    snake_case__						= AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''				)
    image_processor.push_to_hub(
        repo_path_or_name=save_directory / checkpoint_name							,   commit_message='''Add image processor'''							,   use_temp_dir=__lowerCAmelCase							,   )

    print(F"""Pushed {checkpoint_name}"""				)







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase = None							,   __lowerCAmelCase = True				)    ->				List[Any]:
  snake_case__						= '''imagenet-1k-id2label.json'''
  snake_case__						= 1000
  snake_case__						= (1, num_labels)

  snake_case__						= '''huggingface/label-files'''
  snake_case__						= num_labels
  snake_case__						= json.load(open(hf_hub_download(__lowerCAmelCase							,   __lowerCAmelCase							,   repo_type='''dataset'''				)							,   '''r'''				)				)
  snake_case__						= {int(__lowerCAmelCase				): v for k, v in idalabel.items()}

  snake_case__						= idalabel
  snake_case__						= {v: k for k, v in idalabel.items()}

  snake_case__						= partial(__lowerCAmelCase							,   num_labels=__lowerCAmelCase							,   idalabel=__lowerCAmelCase							,   labelaid=__lowerCAmelCase				)

  snake_case__						= {
      '''resnet18''': ImageNetPreTrainedConfig(
          depths=[2, 2, 2, 2]							,   hidden_sizes=[64, 128, 256, 512]							,   layer_type='''basic'''				),
      '''resnet26''': ImageNetPreTrainedConfig(
          depths=[2, 2, 2, 2]							,   hidden_sizes=[256, 512, 1024, 2048]							,   layer_type='''bottleneck'''				),
      '''resnet34''': ImageNetPreTrainedConfig(
          depths=[3, 4, 6, 3]							,   hidden_sizes=[64, 128, 256, 512]							,   layer_type='''basic'''				),
      '''resnet50''': ImageNetPreTrainedConfig(
          depths=[3, 4, 6, 3]							,   hidden_sizes=[256, 512, 1024, 2048]							,   layer_type='''bottleneck'''				),
      '''resnet101''': ImageNetPreTrainedConfig(
          depths=[3, 4, 23, 3]							,   hidden_sizes=[256, 512, 1024, 2048]							,   layer_type='''bottleneck'''				),
      '''resnet152''': ImageNetPreTrainedConfig(
          depths=[3, 8, 36, 3]							,   hidden_sizes=[256, 512, 1024, 2048]							,   layer_type='''bottleneck'''				),
  }

  if model_name:
    convert_weight_and_push(__lowerCAmelCase							,   names_to_config[model_name]							,   __lowerCAmelCase							,   __lowerCAmelCase				)
  else:
    for model_name, config in names_to_config.items():
      convert_weight_and_push(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)
  return config, expected_shape


if __name__ == "__main__":
   lowerCamelCase__							:						List[str]							     =   argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--model_name""",
       default=None,
       type=str,
       help=(
           """The name of the model you wish to convert, it must be one of the supported resnet* architecture,"""
           """ currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted."""
       ),
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""",
       default=None,
       type=Path,
       required=True,
       help="""Path to the output PyTorch model directory.""",
   )
   parser.add_argument(
       """--push_to_hub""",
       default=True,
       type=bool,
       required=False,
       help="""If True, push model and image processor to the hub.""",
   )

   lowerCamelCase__							:						Tuple							     =   parser.parse_args()
   lowerCamelCase__							:						Path							     =   args.pytorch_dump_folder_path
   pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
   convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)






'''simple docstring'''



import argparse
from collections import defaultdict

import yaml


lowerCAmelCase_  :							Tuple             =    'docs/source/en/_toctree.yml'
def        UpperCAmelCase (    A	:					Union[str, Any]					):
							SCREAMING_SNAKE_CASE       :   Dict         =      defaultdict(A					)
							for doc in model_doc:
														counts[doc["local"]] += 1
							SCREAMING_SNAKE_CASE       :   Tuple         =      [key for key, value in counts.items() if value > 1]

							SCREAMING_SNAKE_CASE       :   Tuple         =      []
							for duplicate_key in duplicates:
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      list({doc['''title'''] for doc in model_doc if doc['''local'''] == duplicate_key}					)
														if len(A					) > 1:
																					raise ValueError(
																					    F"""{duplicate_key} is present several times in the documentation table of content at """
																					    '''`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '''
																					    '''others.'''					)
														# Only add this once
														new_doc.append({'''local''': duplicate_key, '''title''': titles[0]}					)

							# Add none duplicate-keys
							new_doc.extend([doc for doc in model_doc if counts[doc['''local''']] == 1]					)

							# Sort
							return sorted(A       ,		key=lambda A					: s["title"].lower()					)
def        UpperCAmelCase (    A	:					Any=False					):
							with open(A       ,		encoding='''utf-8'''					) as f:
														SCREAMING_SNAKE_CASE       :   str         =      yaml.safe_load(f.read()					)

							# Get to the API doc
							SCREAMING_SNAKE_CASE       :   Any         =      0
							while content[api_idx]["title"] != "API":
														api_idx += 1
							SCREAMING_SNAKE_CASE       :   Dict         =      content[api_idx]['''sections''']

							# Then to the model doc
							SCREAMING_SNAKE_CASE       :   List[str]         =      0
							while api_doc[model_idx]["title"] != "Models":
														model_idx += 1

							SCREAMING_SNAKE_CASE       :   Dict         =      api_doc[model_idx]['''sections''']

							SCREAMING_SNAKE_CASE       :   List[Any]         =      [(idx, section) for idx, section in enumerate(A					) if '''sections''' in section]
							SCREAMING_SNAKE_CASE       :   int         =      False
							for idx, modality_doc in modalities_docs:
														SCREAMING_SNAKE_CASE       :   List[str]         =      modality_doc['''sections''']
														SCREAMING_SNAKE_CASE       :   List[str]         =      clean_model_doc_toc(A					)

														if old_modality_doc != new_modality_doc:
																					SCREAMING_SNAKE_CASE       :   Dict         =      True
																					if overwrite:
																												SCREAMING_SNAKE_CASE       :   int         =      new_modality_doc

							if diff:
														if overwrite:
																					SCREAMING_SNAKE_CASE       :   List[Any]         =      model_doc
																					SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      api_doc
																					with open(A       ,		'''w'''       ,		encoding='''utf-8'''					) as f:
																												f.write(yaml.dump(A       ,		allow_unicode=A					)					)
														else:
																					raise ValueError(
																					    '''The model doc part of the table of content is not properly sorted, run `make style` to fix this.'''					)


if __name__ == "__main__":
							lowerCAmelCase_  :							Optional[int]             =    argparse.ArgumentParser()
							parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
							lowerCAmelCase_  :							Any             =    parser.parse_args()

							check_model_doc(args.fix_and_overwrite)




